Research on Software Engineering Test Optimization Method with Intelligent Technology

Abstract

Software testing serves as a critical mechanism for ensuring software quality. The key challenges in software testing involve reducing costs, improving efficiency, and mitigating risks. This paper explores methods for optimizing software engineering testing through
intelligent technologies. Firstly, the research outlines the AI technology with data and algorithm as the core driving force and its extensive
application. On this basis, the key role of machine learning in software defect localization is analyzed, and its basic principle and mainstream
methods are elaborated in detail. Then, the traditional method based on fault tree analysis and its challenges are studied in detail, and a new
paradigm of artificial intelligence enhanced test risk analysis is proposed, which integrates natural language processing, machine learning and
expert system.